Data Collection and Preparation:

– Gather historical data on steel prices from reliable sources such as commodity exchanges, industry reports, and financial databases.
– Include relevant macroeconomic indicators (e.g., GDP growth, industrial production), global trade policies, and other factors influencing steel prices.
– Clean and preprocess data to handle missing values, normalize variables, and prepare datasets for modeling.

Feature Selection and Engineering:

– Identify key predictors (features) that significantly impact steel prices, such as historical prices, supply-demand factors, raw material costs, and economic indicators.
– Engineer additional features, such as moving averages, volatility measures, and lagged variables, to capture temporal patterns and seasonality in price movements.

Model Selection:

– Choose appropriate modeling techniques based on the nature of data and objectives:
– Time Series Models: ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) models for capturing temporal dependencies and seasonality in price data.
– Machine Learning Models: Regression models (e.g., linear regression, ridge regression) and ensemble methods (e.g., random forests, gradient boosting) for capturing nonlinear relationships and interactions among predictors.
– Deep Learning Models: Long Short-Term Memory (LSTM) networks and recurrent neural networks (RNNs) for sequential data and capturing long-term dependencies in price series.

Model Training and Validation:

– Split the dataset into training and validation sets to train models on historical data and evaluate their performance.
– Use techniques such as cross-validation and grid search to optimize model parameters and ensure robustness in predictions.
– Validate models using out-of-sample testing to assess generalization performance and mitigate overfitting.

Model Evaluation Metrics:

– Evaluate model performance using appropriate metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2), to quantify prediction accuracy and reliability.
– Compare different models based on their predictive capabilities and choose the model with the best performance metrics for deployment.

Forecasting and Scenario Analysis:

– Generate forecasts of future steel prices based on trained models, considering different scenarios and economic conditions.
– Conduct sensitivity analysis to assess the impact of changes in input variables (e.g., raw material costs, economic growth rates) on predicted price trajectories and credit risk assessments.

Integration with Credit Risk Management:

– Integrate predicted steel price movements into credit risk management frameworks to assess the potential impact on financial performance, liquidity, and debt servicing capabilities of steel companies.
– Use predictive models to inform decision-making processes, adjust credit limits, and implement risk mitigation strategies in anticipation of future price fluctuations.

Continuous Monitoring and Model Refinement:

– Continuously monitor model performance and update forecasts based on new data and evolving market conditions.
– Refine models over time by incorporating additional features, adjusting parameters, and enhancing predictive accuracy to adapt to changing dynamics in the steel market.